{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib图鉴——进阶柱状图\n",
    "\n",
    "## 公众号：可视化图鉴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3.2\n",
      "1.2.0\n",
      "1.19.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.__version__) #查看Matplotlib版本\n",
    "import pandas as pd\n",
    "print(pd.__version__) #查看pandas版本\n",
    "import numpy as np\n",
    "print(np.__version__) #查看numpy版本\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  #设置中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，代码在以下环境全部通过测试:\n",
    "- Python 3.7.1\n",
    "- Matplotlib == 3.0.2\n",
    "- pandas == 0.23.4\n",
    "- numpy == 1.15.4\n",
    "\n",
    "因版本不同，可能会有部分语法差异，如有报错，请先检查拼写及版本是否一致！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进阶柱状图——瀑布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = [f\"12月{str(i)}日\" for i in range(1, 9)]  #使用f表达式，务必确保Python版本大于等于3.6\n",
    "x = np.array(list(range(len(x_data))))\n",
    "\n",
    "\n",
    "y = [0,3,5,7,6,8,10,9]\n",
    "y1 = [3,2,2,0,2,2,0,0]\n",
    "y2 = [0,0,0,-1,0,0,-1,-2]\n",
    "\n",
    "plt.figure(figsize=(10,7),dpi =100)#设置画布的尺寸\n",
    "\n",
    "\n",
    "plt.bar(x,y1,bottom=y,alpha = 0.7,edgecolor = 'black',label = '公众号')\n",
    "plt.bar(x,y2,bottom=y,alpha = 0.7,edgecolor = 'black',label = '可视化图鉴')\n",
    "\n",
    "plt.xticks(x,x_data, rotation=30,fontsize = 12)\n",
    "\n",
    "plt.ylim(0,11)\n",
    "\n",
    "plt.title('进阶柱状图—瀑布图',fontsize=18) #设置标题\n",
    "\n",
    "plt.xlabel('我是X轴',fontsize = 17) #设置x轴\n",
    "plt.ylabel('我是Y轴',fontsize = 17) #设置y轴\n",
    "\n",
    "plt.legend(loc=2,fontsize=13) \n",
    "# 添加数值显示\n",
    "\n",
    "y3 = [y[i] + y1[i] if y1[i] != 0 else y[i] + y2[i] for i in range(len(x)) ]\n",
    "y_i = [y3[i]+0.3 if y1[i] != 0 else y3[i]-0.6 for i in range(len(x))  ]\n",
    "\n",
    "for i in range(len(x)):\n",
    "    \n",
    "    \n",
    "    plt.text(list(x - 0.05)[i],y_i[i],y3[i],fontsize = 13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
